Press Trust of India05 Apr 2021 10:04:14 IST
Scientists have identified three different types of COVID-19 disease traits in patients, based on their comorbidities, complications and clinical outcomes, a breakthrough that could help target future interventions on those most at risk. The new study, published in the journal PLOS ONE, analyzed electronic health records (EHRs) from 14 hospitals in the Midwestern United States and 60 primary care clinics in the state of Minnesota. According to researchers, including those at the University of Minnesota in the United States, the study included 7,538 patients with COVID-19 confirmed between March 7 and August 25, 2020, of which 1,022 patients required hospitalization.
Almost 60 percent of the patients included in the research presented with what the researchers called “phenotype II”.
They said that about 23 percent of the patients had a “phenotype I” or an “unwanted phenotype,” which was associated with the worst clinical outcomes. The researchers said these patients had the highest level of comorbidities related to heart and kidney dysfunction.
According to the study, 173 patients, or 16.9 percent, had a “phenotype III,” or “favorable phenotype,” which scientists said was associated with better clinical outcomes. While this group had the lowest rate of complications and mortality, scientists said these patients had the highest rate of respiratory comorbidities as well as a 10% higher risk of hospital readmission than others. phenotypes.
Overall, they said phenotypes I and II were associated with 7.30 and 2.57-fold increases in risk of death compared to phenotype III.
Based on the results, scientists said such phenotype-specific medical care could improve COVID-19 outcomes.
However, they believe that more studies are needed to determine the usefulness of these findings in clinical practice.
“Patients do not suffer from COVID-19 uniformly. By identifying the groups affected in the same way, we not only improve our understanding of the disease process, but it allows us to precisely target future interventions on the most affected patients. more at risk, ”the scientists added.